Big data is the name of the game today. Considering that we now live in the digital age, almost virtually all data is now available on the web for our ease and convenience. The number of data circulating online and even offline is growing by the second that it makes perfect sense to talk about big data now. Imagine the number of people using the web these days. From social media to businesses and organizations that have taken this new platform to reach the public, just imagine the unstoppable rise of big data as more processes have become computerized. Since the data we now use is far too many for our limited human capabilities, machines take charge of dealing with big data and understanding what all these data mean. They don’t mean anything if we can’t decipher its practical application.

The Internet of Things has a lot to do with why big data kept on growing bigger and bigger. Various tech devices that hold certain data about their use now easily connect to the web, which is in a way geared toward machine learning, the future of the human race. It means the devices that we have are capable of generating and understanding data without the help of humans. Big data isn’t out there to be left as it is but for us to make something out of it in the hopes of speeding up and improving processes and services and in predicting future trends whether in business, healthcare, etc.

The big promise behind big data

Several factors have fueled the rise of big data. People now store and keep more information than ever before due to widespread digitization of paper records among businesses. The proliferation of sensor-based Internet of Things (IoT) devices has led to a corresponding rise in the number of applications based on artificial intelligence (AI), which is an enabling technology for machine learning. These devices generate their own data without human intervention.

A misconception about big data is that the term refers solely to the size of the data set. Although this is true in the main, the science behind big data is more focused. The intention is to mine specific subsets of data from multiple, large storage volumes. This data may be widely dispersed in different systems and may not have an obvious correlation. The objective is to unify the data with structure and intelligence to allow it to be rapidly analyzed.


The role and use of big data are so prevalent in today’s world. Even in your use of social media, you’d see how popular search engines and social networking sites make use of big data in organizing everything and in the posting of advertisements depending on specific user behaviors and clicking patterns. And as such, big data is housed in big servers that are capable of safely storing it and pulling it out in short notice for analysis and use.

Big data can bring an organization a competitive advantage from large-scale statistical analysis of the data or its metadata. In a big data environment, the analytics mostly operate on a circumscribed set of data, using a series of data mining-based predictive modeling forecasts to gauge customer behaviors or the likelihood of future events.

Statistical big data analysis and modeling is gaining adoption in a cross-section of industries, including aerospace, environmental science, energy exploration, financial markets, genomics, healthcare and retailing. A big data platform is built for much greater scale, speed and performance than traditional enterprise storage. Also, in most cases, big data storage targets a much more limited set of workloads on which it operates.


The most important factor for big data to be useful and relevant in our modern times, the structure must be high up on its list or else we have no use for haphazardly stored data that we don’t have much use of after. Now that big data comes from virtually everywhere in the globe, it is a must for these large chunks of data to be stored in servers that can shed light to what this information and numbers actually mean. Variety, velocity, and data volume are three things that are crucial to big data as this information are often produced in large volumes at different speeds using a variety of formats. With trillions of data produced each second, it is a must that everything must be streamlined so we can easily transition to machine learning someday, the ultimate goal in the tech world.

When data is already up there in the clouds or stored in big servers, you may no longer have to worry about common data storage and loss issues that the common people face on a regular basis. Meanwhile, smaller privately-owned servers can experience technical glitches regarding data use. Understanding QNap RAID data recovery may come in helpful and help you overcome data recovery problems without spending a fortune.